1. Technical Field
The present disclosure relates to fiber tracking, and more particularly to fiber tracking in a diffusion tensor image dataset.
2. Description of Related Art
Diffusion tensor image scans comprise at least six gradient directions, sufficient to determine a diffusion tensor in, for example, a brain scan. From the diffusion tensor, diffusion anisotropy measures such as the Fractional Anisotropy (FA) can be determined. Moreover, the principal direction of the diffusion tensor can be used to infer white-matter connectivity of the brain and model it as a tract.
Fiber tracking is the process of detecting fiber pathways in a DTI dataset by following directional information contained in the diffusion tensors. Various methods can be applied to determine a set of tracks to represent white-matter connectivity of the brain; typically they rely on the placement of starting points or seed points, and tracking fiber pathways from these starting positions according to direction and anisotropy data determined from the diffusion tensors.
Seed points can be placed manually by the user by selecting a region of interest, or automatically by selecting a subset of voxels that match a specific criterion (e.g. FA threshold). In both cases, the resulting set of fiber pathways can be highly redundant, wherein many fibers can have similar trajectories, and some relevant pathways can be ignored if the proper seed points were not selected. Furthermore, these methods can yield very large sets of seed points, making the fiber tracking process and any further data analysis computationally expensive.
Therefore, a need exists for a system and method for automatic detection of fiber pathways in a DTI dataset.